Informationization is a prevailing trend in today's world. The increasing demand for information in decision-making processes poses significant challenges for investigation activities, particularly in terms of effectively allocating limited resources to plan investigation programs. This paper addresses the investigation path planning problem by formulating it as a multi-traveling salesman problem (MTSP). Our objective is to minimize costs, and to achieve this, we propose a chaotic artificial fish swarm algorithm based on multiple population differential evolution (DE-CAFSA). To overcome the limitations of the artificial fish swarm algorithm, such as low optimization accuracy and the inability to consider global and local information, we incorporate adaptive field of view and step size adjustments, replace random behavior with the 2-opt operation, and introduce chaos theory and sub-optimal solutions to enhance optimization accuracy and search performance. Additionally, we integrate the differential evolution algorithm to create a hybrid algorithm that leverages the complementary advantages of both approaches. Experimental results demonstrate that DE-CAFSA outperforms other algorithms on various public datasets of different sizes, as well as showcasing excellent performance on the examples proposed in this study.

翻译：信息化是当今世界的主流趋势。决策过程中对信息的需求日益增长，给调查活动带来了重大挑战，特别是在有效分配有限资源以规划调查方案方面。本文将调查路径规划问题建模为多旅行商问题（MTSP），旨在最小化成本。为此，我们提出了一种基于多种群差分进化的混沌人工鱼群算法（DE-CAFSA）。为克服人工鱼群算法优化精度低、无法兼顾全局与局部信息等局限性，我们引入了自适应视野与步长调整，用2-opt操作替代随机行为，并引入混沌理论与次优解以提升优化精度与搜索性能。此外，我们融合差分进化算法，构建了一种兼具两者互补优势的混合算法。实验结果表明，DE-CAFSA在多种不同规模的公开数据集上优于其他算法，并在本研究提出的实例中展现出卓越性能。